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Ask consequential choices

grill_me_ask

Ask one multiple-choice question per card to clarify requirements and automatically save the selected answer into the conversation.

Instructions

Ask one round in one card, with at most one question visible at a time. Never stack pending cards. Last answer submits directly. Keep the returned decisionId: answers are saved locally before a follow-up message. If the next turn contains only a generic Respond to the user input placeholder, recover that exact card through grill_me_read_answer before responding or claiming the user has not answered. Continue unrelated work while waiting. If UI does not render, ask in text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
localeNoauto
optionsNo
multipleNo
questionNo
questionsNo
recommendedIndexNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0-beta.1

TDQS

A3.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden and does so thoroughly: it discloses local persistence of answers, direct submission of the last answer, the need to retain decisionId, the recovery protocol via grill_me_read_answer, and a fallback when UI does not render. This is rich, honest behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Six dense sentences, all operational and front-loaded with the core behavior before edge cases. There is no filler; every sentence earns its place, and the structure is easy to scan despite being a single paragraph.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The behavioral workflow is well covered, including recovery and fallback scenarios. However, with no output schema and no parameter descriptions, the return value shape and parameter construction details remain underspecified, so the description is not fully complete for an agent invoking the tool from scratch.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds almost no meaning for locale, multiple, recommendedIndex, or the question/options structures. It only implies multiple questions via 'one question visible at a time' and 'last answer submits,' leaving agents to infer parameter intent from names and schema constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action: ask a round of questions in a single card, with one question visible at a time. It distinguishes itself from the sibling grill_me_read_answer through the recovery instruction, though it does not explicitly contrast with every sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear operational guidance: one question per card, never stack pending cards, last answer submits directly, and a precise condition for using grill_me_read_answer when a generic placeholder appears. It lacks explicit when-not-to-use guidance against grill_me_submit_answer or grill_me_preferences, so not a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.